Aging Cell (2015) 14, pp112–121
Doi: 10.1111/acel.12275
Factors affecting longitudinal trajectories of plasma sphingomyelins: the Baltimore Longitudinal Study of Aging Michelle M. Mielke,1 Veera Venkata Ratnam Bandaru,2 Dingfen Han,3 Yang An,4 Susan M. Resnick,4 Luigi Ferrucci4 and Norman J. Haughey2,3 1
Department of Health Science Research and Neurology, Mayo Clinic, Rochester, MN, USA 2 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA 3 Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA 4 Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Summary Sphingomyelin metabolism has been linked to several diseases and to longevity. However, few epidemiological studies have quantified individual plasma sphingomyelin species (identified by acyl-chain length and saturation) or their relationship between demographic factors and disease processes. In this study, we determined plasma concentrations of distinct sphingomyelin species in 992 individuals, aged 55 and older, enrolled in the Baltimore Longitudinal Study of Aging. Participants were followed, with serial measures, up to 6 visits and 38 years (3972 total samples). Quantitative analyses were performed on a highperformance liquid chromatography-coupled electrospray ionization tandem mass spectrometer. Linear mixed models were used to assess variation in specific sphingomyelin species and associations with demographics, diseases, medications or lifestyle factors, and plasma cholesterol and triglyceride levels. We found that most sphingomyelin species increased with age. Women had higher plasma levels of all sphingomyelin species and showed steeper trajectories of age-related increases compared to men. African Americans also showed higher circulating sphingomyelin concentrations compared to Caucasians. Diabetes, smoking, and plasma triglycerides were associated with lower levels of many sphingomyelins and dihydrosphingomyelins. Notably, these associations showed specificity to sphingomyelin acyl-chain length and saturation. These results demonstrate that longitudinal changes in circulating sphingomyelin levels are influenced by age, sex, race, lifestyle factors, and diseases. It will be important to further establish the intra-individual age- and sex-specific changes in each sphingomyelin species in relation to disease onset and progression.
Aging Cell
Key words: aging; sphingomyelin; dihydrosphingomyelin; human; longitudinal; sex differences.
Correspondence Dr. Michelle M. Mielke, Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, 200 First Street S.W., Rochester, MN 55905 USA. Tel.: +1 507 284 5545; fax: +1 507 284 1516; e-mail: [email protected]
Introduction The plasma lipidome is composed of numerous lipids that comprise specific variations in both structure and function. It has been increasing recognized that lipids are associated with the development and progression of many diseases, as well as aging and longevity (Vaarhorst et al., 2011; Yu et al., 2012; Gonzalez-Covarrubias et al., 2013). Sphingomyelins are among the most abundant lipids in many mammalian cells, tissues, and the circulation. In cells, sphingomyelins are central to the assemblage of lipid rafts and ordered membrane microdomains (Simons & Ikonen, 1997; Simons & Vaz, 2004; Quinn & Wolf, 2009). The biophysical properties of these lipids play important roles in the functional regulation of membrane-spanning proteins (Contreras et al., 2012) and in cholesterol homeostasis (Gatt & Bierman, 1980; Slotte & Bierman, 1988). Sphingomyelins are also rate-limiting precursors for other sphingolipids classes, such as ceramides, that are directly involved in a variety of cell-signaling events (Milhas et al., 2010). Niemann–Pick disease types A and B are genetic lipid storage disorders resulting from the lysosomal accumulation of sphingomyelin. Sphingomyelin metabolism has also been linked to other diseases including coronary artery disease (Jiang et al., 2000), ovarian endometriosis (Vouk et al., 2012), and cancer (Kim et al., 2013; Petersen et al., 2013), and also with longevity (GonzalezCovarrubias et al., 2013). The majority of studies that examined sphingomyelins have focused on total sphingomyelin levels. However, there are a variety of sphingomyelin species (identified by acyl-chain length and saturation), with differential subcellular, cellular and tissue distributions that contribute to distinct cellular functions (Hannun & Obeid, 2011). With improved understanding of the role of the specific carbon-chain lengths in disease processes at the biochemical level, there is a need to translate this work to humans. There are little data on how plasma sphingomyelin and dihydrosphingomyelin levels are affected by age, sex, race, and changes in disease status within individuals or in the population. Previous, carefully validated studies of individual sphingomyelin species have been based on small samples of 10–15 people and young individuals (i.e., 100 mg dL 1 and/or a 2-h glucose >140 mg dL 1 were defined as having prediabetes. Medication use was verified at each visit. Plasma total cholesterol and triglycerides were determined by an enzymatic method (Abbott Laboratories ABA-200 ATC Biochromatic Analyzer, Irving, TX, USA). Apolipoprotein E (APOE) genotype was determined in approximately half of the sample set using polymerase chain reaction amplification of leukocyte deoxyribonucleic acid followed by HhaI digestion and product characterization (Hixson & Vernier, 1990) and by TaqMan in the remaining half, relying on several single nucleotide polymorphisms around the APOE gene (Koch et al., 2002).
Lipid extraction and LC/ESI/MS/MS analysis A crude lipid extraction of plasma was obtained using a modified Bligh and Dyer procedure as previously described (Haughey et al., 2004; Bandaru et al., 2013). Sphingomyelin C12:0 (1.3 lg mL 1; Avanti Polar Lipids, Alabaster, Alabama) was included in the extraction solvent as an internal standard. The chloroform layer containing a crude lipid extract was dried in a nitrogen evaporator (Organomation Associates Inc.,
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Plasma Sphingomyelins by age and sex, M. M. Mielke et al. 119
(A)
Table 5 Multiple reaction monitoring (MRM) transitions for molecular species of sphingomyelin precursor and fragment ions Precursor/fragment ion m/z
d18:1/12:0 d18:1/16:0 d18:1/18:0 d18:1/20:0 d18:1/22:0 d18:1/24:0 d18:1/26:0 d18:1/16:1 d18:1/18:1 d18:1/20:1 d18:1/22:1 d18:1/24:1 d18:1/26:1
647.7/184.4* 703.8/184.4 732.1/184.4 759.8/184.4 787.9/184.4 815.9/184.4 843.9/184.4 701.5/184.4 729.4/184.4 757.4/184.4 785.4/184.4 813.9/184.4 841.9/184.4
Dihydrosphingomyelins
Precursor/fragment ion m/z
d18:0/16:0 d18:0/18:0 d18:0/20:0 d18:0/22:0 d18:0/24:0 d18:0/26:0
706.9/184.4 734.8/184.4 761.8/184.4 789.9/184.4 817.9/184.4 845.9/184.4
*Internal standard.
d18:1/16:0 d18:1/18:0 d18:1/20:0 d18:1/22:0 d18:1/24:0
50 40 30 20 10 0
0
200 400 600 800 1000 Concentration(ng mL–1)
(B) 6 × 105
Original runs Original + reruns replaced
4 × 105
2 × 105
0 0
1000
2000
3000
4000
Sample number
(C) SMC16:0 (ng mL–1)
Sphingomyelins
Arearatio (Analyte/IS)
were determined by fitting the identified sphingomyelin and dihydrosphingomyelin species to these standard curves based on acyl-chain length (i.e., concentrations of C16:0 and C16:1 were both determined based on the C16:0 standard curve). Instrument efficiency was closely monitored using a series of internal standards run daily. Area under the curve for these standards was plotted weekly to track instrument efficiency. Runs were stopped when consistent deviations of more than 30% from the median internal standard values were noted, and the instrument was recalibrated. Individual samples in which the internal standard deviated more than 30% of the median internal standard value were rerun. Using these criteria, approximately 20% of samples required reanalysis. Data that produced internal standard values closest to the overall median value were incorporated in the final analysis (Fig. 3B). Instrument control and quantification were performed using Analyst 1.4.2 and MultiQuant software (AB Sciex Inc., Thornhill, ON, Canada). Intra-day coefficient of variation (CV) was determined by analyzing 5 pooled samples 6 times. Intra-day CVs were as follows: C16:0 (2.1%), C18:0(5.7%), C20:0 (2.1%), C22:0 (3.0%), C24:0 (3.6%), C16:1 (2.1%), C18:1 (3.6%), C20:1 (9.6%), C22:1 (4.0%), C24:1 (2.4%) and dihydrosphingomyelins (DHSM) C16:0 (4.0%), C18:0 (3.7%), C22:0 (7.4%), and C24:0 (8.6%). Inter-day CVs were
Analyte Area (C12:0)
Berlin, MA, USA) and stored at 80 °C. Dried extracts were resuspended in pure methanol just prior to analysis. Analyses of sphingomyelins were performed on a triple quadrupole mass spectrometer (API3000, AB Sciex Inc., Thornhill, Ontario, Canada) using instrument parameters similar to those described in previous studies (Bandaru et al., 2007, 2011, 2013). Samples were injected using an Agilent 1100 high-pressure liquid chromatography (HPLC) (Agilent Technologies, Inc., Santa Clara CA, USA) equipped with a reverse phase C18 column (Phenomenex, Torrance, CA, USA). Sphingomyelin species were separated by gradient elution at the flow rate of 0.7 mL min 1. Mobile phases consisted of A: 60% methanol, 39% H2O, 1% formic acid with 5 mM ammonium formate; and B: 99% methanol, 1% formic acid, and 5 mM ammonium formate. Gradient conditions were as follows: 60% B for 0.01 min, a gradual increase to 100% B over the next 0.49 min, and hold at 100% B for 3 min. Decline from 100% to 0% B during the next 0.01 min, hold at 0% B for 0.99 min, increase from 0% to 60% B for 0.5 min and hold at 60% B for the final 0.5 min. The eluted sample was injected into the ion source where the detection of each sphingomyelin species was conducted by multiple reaction monitoring (MRM) in positive mode. Detailed MRM transitions of individual molecular species for sphingomyelin precursor and fragment ions are provided in Table 5. The ion spray voltage (V) was 5500 at a temperature of 80 °C with a nebulizer gas of 9 psi, curtain gas of 8 psi, and the collision gas set at 10 psi. The declustering potential was 60 V, the focusing potential 300 V, the entrance potential 10 V, the collision energy 30 V, and the collision cell exit potential 10 V. MS/MS scanned from 300 to 1000 atomic mass units (amu) per second with steps of 0.1 amu. Eight-point calibration curves (0.1 to 1000 ng mL 1) were constructed by plotting the area under the curve for sphingomyelin C16:0, C18:0, C20:0, C22:0, and C24:0 (Avanti polar lipids, Alabaster, AL, USA) prepared in pure methanol normalized to the C12:0 internal standard. The correlation coefficients (R2) obtained were >0.999 (Fig. 3A). Concentrations of sphingomyelins in each sample
2.0 × 106 1.5 × 106 1.0 × 106 5.0 × 105 0.0
1963
1986
2009
Year of sample collection Fig. 3 (A) Five-point standard curves for the indicated SM species. (B) Initial and rerun data showing values obtained for the internal standard SM C12:0. (C) SM C16:0 scatterplot by date of visit showing a random distribution, suggesting that SM was stable during long-term storage of plasma.
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
120 Plasma Sphingomyelins by age and sex, M. M. Mielke et al. determined using five repeat measurements of 10 pooled samples over a 3-month period (the time required for analysis of all study samples). Interday CVs for sphingomyelin species were as follows: C18:0/C16:0 (12%), C18:0/C18:0 (20%), C18:0/C20:0 (24%), C18:0/C22:0 (12%), C18:0/C24:0 (8%),C18:0/C16:1 (12%), C18:0/C18:1 (16%), C18:0/ C20:1 (20%), C18:0/C22:1 (28%), C18:0/C24:1 (12%),C18:1/C16:0 (14%), C18:1/C18:0 (8%), and C18:1/C24:0 (6%). Recovery was determined by comparing C12:0 internal standard levels in extracted plasma to equal amounts of C12:0 sphingomyelin prepared in pure methanol. The average extraction recovery obtained was 93%. As the dates of sample collection for this study ranged from 1968 to 2009 and were run in random order over the 3 months, we estimated sample stability by arranging data for each sphingomyelin species by date of study visit. We reasoned that sample degradation or continued enzymatic activity would be manifest by increasing or decreasing trends in analyte concentrations coincident with storage time. When arranged by date of visit, each species showed a random scatter (Fig. 3C shows sphingomyelin C16:0), suggesting that sphingomyelin content of plasma was stable with long-term 80 °C storage.
Research Program of the National Institutes of Health/National Institute on Aging.
Author contributions MMM conceived the analyses and contributed to the statistical analyses, interpretation of results, and wrote the initial draft of the paper. VVRB contributed to the mass spectrometry analyses and interpretation of the data. DH and YA contributed to the statistical analyses, interpretation of results, and writing the manuscript. RS and LF contributed to the study design of the Baltimore Longitudinal Study on Aging, data collection, interpretation of results and writing of the manuscript. NH contributed to the mass spectrometry analyses, interpretation of results and writing of the manuscript.
Conflict of interest The authors have no conflict of interests to declare.
References Statistical analyses Sex differences in baseline demographic and health-related characteristics were examined using Fisher’s exact test for categorical variables and t-tests or ANOVA for continuous variables. We examined all sphingomyelin species for outliers. As normal ranges for sphingomyelins in plasma are not yet known, we conservatively defined an outlier as any sphingomyelin species with a concentration of more than 3 interquartile ranges (IQR, 25th percentile–75th percentile) from the median. Within each class of the plasma sphingomyelin and dihydrosphingomyelin, a mean of 3% of samples were outside this range and excluded from the analyses. Some individuals had missing covariates for a specific visit. Missing values for the covariates described below ranged from 0% to 20.1% (for APOE genotype only) of the 3972 total visits. As the BLSA follows individuals for decades, we imputed the missing covariates for each individual and visit using information from neighboring visits. This allowed us to utilize the largest number of samples. We used linear mixed models to account for the longitudinal nature of the data and to model the trajectories for individual sphingomyelin species over time using time-dependent analyses. We used sphingomyelin class and species as the dependent variable. Based on the literature, we initially examined the following predictors: age, age squared, sex, race (white vs. African American), education, the presence of an APOE e4 allele (yes vs. no), BMI, WHR, smoking (never versus ever), depressive symptoms, triglycerides, cholesterol, medications (e.g., statins), and medical conditions (e.g., diabetes, hypertension, CKD), and the interaction term between these predictors and age. We forced age, age squared, sex, and BMI into all models based on the literature and our observations of significant associations with the sphingomyelins in univariate analyses. We then used backward selection with P < 0.10 to determine which variables to include in the final model for each sphingomyelin class and species. All analyses were conducted using STATA Version 11.0 (StataCorp, College Station, TX, USA).
Acknowledgments This work was supported by a grant from the National Institutes of Health/National Institute on Aging (U01 AG37526) and by the Intramural
Bandaru VV, McArthur JC, Sacktor N, Cutler RG, Knapp EL, Mattson MP, Haughey NJ (2007) Associative and predictive biomarkers of dementia in HIV-1-infected patients. Neurology 68, 1481–1487. Bandaru VV, Patel N, Ewaleifoh O, Haughey NJ (2011) A failure to normalize biochemical and metabolic insults during morphine withdrawal disrupts synaptic repair in mice transgenic for HIV-gp120. J. Neuroimmune Pharmacol. 6, 640– 649. Bandaru VV, Mielke MM, Sacktor N, McArthur JC, Grant I, Letendre S, Chang L, Wojna V, Pardo C, Calabresi P, Munsaka S, Haughey NJ (2013) A lipid storage-like disorder contributes to cognitive decline in HIV-infected subjects. Neurology 81, 1492–1499. Contreras FX, Ernst AM, Haberkant P, Bjorkholm P, Lindahl E, Gonen B, Tischer C, Elofsson A, von Heijne G, Thiele C, Pepperkok R, Wieland F, Brugger B (2012) Molecular recognition of a single sphingolipid species by a protein’s transmembrane domain. Nature 481, 525–529. Deeley JM, Hankin JA, Friedrich MG, Murphy RC, Truscott RJ, Mitchell TW, Blanksby SJ (2010) Sphingolipid distribution changes with age in the human lens. J. Lipid Res. 51, 2753–2760. Fernandez C, Sandin M, Sampaio JL, Almgren P, Narkiewicz K, Hoffmann M, Hedner T, Wahlstrand B, Simons K, Shevchenko A, James P, Melander O (2013) Plasma lipid composition and risk of developing cardiovascular disease. PLoS ONE 8, e71846. Gatt S, Bierman EL (1980) Sphingomyelin suppresses the binding and utilization of low density lipoproteins by skin fibroblasts. J. Biol. Chem. 255, 3371–3376. Gonzalez-Covarrubias V, Beekman M, Uh HW, Dane A, Troost J, Paliukhovich I, van der Kloet FM, Houwing-Duistermaat J, Vreeken RJ, Hankemeier T, Slagboom EP (2013) Lipidomics of familial longevity. Aging Cell 12, 426–434. Hammad SM, Pierce JS, Soodavar F, Smith KJ, Al Gadban MM, Rembiesa B, Klein RL, Hannun YA, Bielawski J, Bielawska A (2010) Blood sphingolipidomics in healthy humans: impact of sample collection methodology. J. Lipid Res. 51, 3074–3087. Hannun YA, Obeid LM (2011) Many ceramides. J. Biol. Chem. 286, 27855–27862. Haughey NJ, Cutler RG, Tamara A, McArthur JC, Vargas DL, Pardo CA, Turchan J, Nath A, Mattson MP (2004) Perturbation of sphingolipid metabolism and ceramide production in HIV-dementia. Ann. Neurol. 55, 257–267. Haughey NJ, Bandaru VV, Bae M, Mattson MP (2010) Roles for dysfunctional sphingolipid metabolism in Alzheimer’s disease neuropathogenesis. Biochim. Biophys. Acta 1801, 878–886. Hixson JE, Vernier DT (1990) Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J. Lipid Res. 31, 545–548. Holzer M, Trieb M, Konya V, Wadsack C, Heinemann A, Marsche G (2013) Aging affects high-density lipoprotein composition and function. Biochim. Biophys. Acta 1831, 1442–1448. Jiang XC, Paultre F, Pearson TA, Reed RG, Francis CK, Lin M, Berglund L, Tall AR (2000) Plasma sphingomyelin level as a risk factor for coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 20, 2614–2618.
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Plasma Sphingomyelins by age and sex, M. M. Mielke et al. 121 Kim IC, Lee JH, Bang G, Choi SH, Kim YH, Kim KP, Kim HK, Ro J (2013) Lipid profiles for HER2-positive breast cancer. Anticancer Res. 33, 2467–2472. Koch W, Ehrenhaft A, Griesser K, Pfeufer A, Muller J, Schomig A, Kastrati A (2002) TaqMan systems for genotyping of disease-related polymorphisms present in the gene encoding apolipoprotein E. Clin. Chem. Lab. Med. 40, 1123–1131. Kuikka M, Ramstedt B, Ohvo-Rekila H, Tuuf J, Slotte JP (2001) Membrane properties of D-erythro-N-acyl sphingomyelins and their corresponding dihydro species. Biophys. J . 80, 2327–2337. Mielke MM, Haughey NJ, Bandaru VV, Weinberg DD, Darby E, Zaidi N, Pavlik V, Doody RS, Lyketsos CG (2011) Plasma sphingomyelins are associated with cognitive progression in Alzheimer’s disease. J. Alzheimers Dis. 27, 259–269. Milhas D, Clarke CJ, Hannun YA (2010) Sphingomyelin metabolism at the plasma membrane: implications for bioactive sphingolipids. FEBS Lett. 584, 1887–1894. Montefusco DJ, Chen L, Matmati N, Lu S, Newcomb B, Cooper GF, Hannun YA, Lu X (2013) Distinct signaling roles of ceramide species in yeast revealed through systematic perturbation and systems biology analyses. Sci. Signal 6, rs14. Nelson JC, Jiang XC, Tabas I, Tall A, Shea S (2006) Plasma sphingomyelin and subclinical atherosclerosis: findings from the multi-ethnic study of atherosclerosis. Am. J. Epidemiol. 163, 903–912. Petersen NH, Olsen OD, Groth-Pedersen L, Ellegaard AM, Bilgin M, Redmer S, Ostenfeld MS, Ulanet D, Dovmark TH, Lonborg A, Vindelov SD, Hanahan D, Arenz C, Ejsing CS, Kirkegaard T, Rohde M, Nylandsted J, Jaattela M (2013) Transformation-associated changes in sphingolipid metabolism sensitize cells to lysosomal cell death induced by inhibitors of acid sphingomyelinase. Cancer Cell 24, 379–393. Quinn PJ, Wolf C (2009) The liquid-ordered phase in membranes. Biochim. Biophys. Acta 1788, 33–46. Schlitt A, Blankenberg S, Yan D, von Gizycki H, Buerke M, Werdan K, Bickel C, Lackner KJ, Meyer J, Rupprecht HJ, Jiang XC (2006) Further evaluation of plasma sphingomyelin levels as a risk factor for coronary artery disease. Nutr. Metab. 3, 5. Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R (1989) Studies in the distribution of body fat: I. Effects of age, sex, and obesity. J. Gerontol. 44, M66–M73.
Shock NW, Greulich RC, Andres R, Arenberg D, Costa PT Jr, Lakatta EG, Tobin JD (1984) Normal Human Aging: The Baltimore Longitudinal Study of Aging. Washington, DC: U.S. Government Printing Office. Simons K, Ikonen E (1997) Functional rafts in cell membranes. Nature 387, 569– 572. Simons K, Vaz WL (2004) Model systems, lipid rafts, and cell membranes. Annu. Rev. Biophys. Biomol. Struct. 33, 269–295. Slotte JP, Bierman EL (1988) Depletion of plasma-membrane sphingomyelin rapidly alters the distribution of cholesterol between plasma membranes and intracellular cholesterol pools in cultured fibroblasts. Biochem. J. 250, 653–658. Vaarhorst AA, Beekman M, Suchiman EH, van Heemst D, Houwing-Duistermaat JJ, Westendorp RG, Slagboom PE, Heijmans BT (2011) Lipid metabolism in long-lived families: the Leiden Longevity Study. Age (Dordr) 33, 219–227. Vieira CR, Munoz-Olaya JM, Sot J, Jimenez-Baranda S, Izquierdo-Useros N, Abad JL, Apellaniz B, Delgado R, Martinez-Picado J, Alonso A, Casas J, Nieva JL, Fabrias G, Manes S, Goni FM (2010) Dihydrosphingomyelin impairs HIV-1 infection by rigidifying liquid-ordered membrane domains. Chem. Biol. 17, 766–775. Vouk K, Hevir N, Ribic-Pucelj M, Haarpaintner G, Scherb H, Osredkar J, Moller G, Prehn C, Rizner TL, Adamski J (2012) Discovery of phosphatidylcholines and sphingomyelins as biomarkers for ovarian endometriosis. Hum. Reprod. 27, 2955–2965. Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, Comuzzie AG, Mahaney MC, Jowett JB, Shaw J, Curran JE, Blangero J, Meikle PJ (2013) Plasma lipid profiling in a large population-based cohort. J. Lipid Res. 54, 2898– 2908. Yeboah J, McNamara C, Jiang XC, Tabas I, Herrington DM, Burke GL, Shea S (2010) Association of plasma sphingomyelin levels and incident coronary heart disease events in an adult population: Multi-Ethnic Study of Atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 30, 628–633. Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C, Romisch-Margl W, Lattka E, Gieger C, Soranzo N, Heinrich J, Standl M, Thiering E, Mittelstrass K, Wichmann HE, Peters A, Suhre K, Li Y, Adamski J, Spector TD, Illig T, Wang-Sattler R (2012) Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967.
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Copyright of Aging Cell is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
Doi: 10.1111/acel.12275
Factors affecting longitudinal trajectories of plasma sphingomyelins: the Baltimore Longitudinal Study of Aging Michelle M. Mielke,1 Veera Venkata Ratnam Bandaru,2 Dingfen Han,3 Yang An,4 Susan M. Resnick,4 Luigi Ferrucci4 and Norman J. Haughey2,3 1
Department of Health Science Research and Neurology, Mayo Clinic, Rochester, MN, USA 2 Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA 3 Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA 4 Intramural Research Program, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
Summary Sphingomyelin metabolism has been linked to several diseases and to longevity. However, few epidemiological studies have quantified individual plasma sphingomyelin species (identified by acyl-chain length and saturation) or their relationship between demographic factors and disease processes. In this study, we determined plasma concentrations of distinct sphingomyelin species in 992 individuals, aged 55 and older, enrolled in the Baltimore Longitudinal Study of Aging. Participants were followed, with serial measures, up to 6 visits and 38 years (3972 total samples). Quantitative analyses were performed on a highperformance liquid chromatography-coupled electrospray ionization tandem mass spectrometer. Linear mixed models were used to assess variation in specific sphingomyelin species and associations with demographics, diseases, medications or lifestyle factors, and plasma cholesterol and triglyceride levels. We found that most sphingomyelin species increased with age. Women had higher plasma levels of all sphingomyelin species and showed steeper trajectories of age-related increases compared to men. African Americans also showed higher circulating sphingomyelin concentrations compared to Caucasians. Diabetes, smoking, and plasma triglycerides were associated with lower levels of many sphingomyelins and dihydrosphingomyelins. Notably, these associations showed specificity to sphingomyelin acyl-chain length and saturation. These results demonstrate that longitudinal changes in circulating sphingomyelin levels are influenced by age, sex, race, lifestyle factors, and diseases. It will be important to further establish the intra-individual age- and sex-specific changes in each sphingomyelin species in relation to disease onset and progression.
Aging Cell
Key words: aging; sphingomyelin; dihydrosphingomyelin; human; longitudinal; sex differences.
Correspondence Dr. Michelle M. Mielke, Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, 200 First Street S.W., Rochester, MN 55905 USA. Tel.: +1 507 284 5545; fax: +1 507 284 1516; e-mail: [email protected]
Introduction The plasma lipidome is composed of numerous lipids that comprise specific variations in both structure and function. It has been increasing recognized that lipids are associated with the development and progression of many diseases, as well as aging and longevity (Vaarhorst et al., 2011; Yu et al., 2012; Gonzalez-Covarrubias et al., 2013). Sphingomyelins are among the most abundant lipids in many mammalian cells, tissues, and the circulation. In cells, sphingomyelins are central to the assemblage of lipid rafts and ordered membrane microdomains (Simons & Ikonen, 1997; Simons & Vaz, 2004; Quinn & Wolf, 2009). The biophysical properties of these lipids play important roles in the functional regulation of membrane-spanning proteins (Contreras et al., 2012) and in cholesterol homeostasis (Gatt & Bierman, 1980; Slotte & Bierman, 1988). Sphingomyelins are also rate-limiting precursors for other sphingolipids classes, such as ceramides, that are directly involved in a variety of cell-signaling events (Milhas et al., 2010). Niemann–Pick disease types A and B are genetic lipid storage disorders resulting from the lysosomal accumulation of sphingomyelin. Sphingomyelin metabolism has also been linked to other diseases including coronary artery disease (Jiang et al., 2000), ovarian endometriosis (Vouk et al., 2012), and cancer (Kim et al., 2013; Petersen et al., 2013), and also with longevity (GonzalezCovarrubias et al., 2013). The majority of studies that examined sphingomyelins have focused on total sphingomyelin levels. However, there are a variety of sphingomyelin species (identified by acyl-chain length and saturation), with differential subcellular, cellular and tissue distributions that contribute to distinct cellular functions (Hannun & Obeid, 2011). With improved understanding of the role of the specific carbon-chain lengths in disease processes at the biochemical level, there is a need to translate this work to humans. There are little data on how plasma sphingomyelin and dihydrosphingomyelin levels are affected by age, sex, race, and changes in disease status within individuals or in the population. Previous, carefully validated studies of individual sphingomyelin species have been based on small samples of 10–15 people and young individuals (i.e., 100 mg dL 1 and/or a 2-h glucose >140 mg dL 1 were defined as having prediabetes. Medication use was verified at each visit. Plasma total cholesterol and triglycerides were determined by an enzymatic method (Abbott Laboratories ABA-200 ATC Biochromatic Analyzer, Irving, TX, USA). Apolipoprotein E (APOE) genotype was determined in approximately half of the sample set using polymerase chain reaction amplification of leukocyte deoxyribonucleic acid followed by HhaI digestion and product characterization (Hixson & Vernier, 1990) and by TaqMan in the remaining half, relying on several single nucleotide polymorphisms around the APOE gene (Koch et al., 2002).
Lipid extraction and LC/ESI/MS/MS analysis A crude lipid extraction of plasma was obtained using a modified Bligh and Dyer procedure as previously described (Haughey et al., 2004; Bandaru et al., 2013). Sphingomyelin C12:0 (1.3 lg mL 1; Avanti Polar Lipids, Alabaster, Alabama) was included in the extraction solvent as an internal standard. The chloroform layer containing a crude lipid extract was dried in a nitrogen evaporator (Organomation Associates Inc.,
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Plasma Sphingomyelins by age and sex, M. M. Mielke et al. 119
(A)
Table 5 Multiple reaction monitoring (MRM) transitions for molecular species of sphingomyelin precursor and fragment ions Precursor/fragment ion m/z
d18:1/12:0 d18:1/16:0 d18:1/18:0 d18:1/20:0 d18:1/22:0 d18:1/24:0 d18:1/26:0 d18:1/16:1 d18:1/18:1 d18:1/20:1 d18:1/22:1 d18:1/24:1 d18:1/26:1
647.7/184.4* 703.8/184.4 732.1/184.4 759.8/184.4 787.9/184.4 815.9/184.4 843.9/184.4 701.5/184.4 729.4/184.4 757.4/184.4 785.4/184.4 813.9/184.4 841.9/184.4
Dihydrosphingomyelins
Precursor/fragment ion m/z
d18:0/16:0 d18:0/18:0 d18:0/20:0 d18:0/22:0 d18:0/24:0 d18:0/26:0
706.9/184.4 734.8/184.4 761.8/184.4 789.9/184.4 817.9/184.4 845.9/184.4
*Internal standard.
d18:1/16:0 d18:1/18:0 d18:1/20:0 d18:1/22:0 d18:1/24:0
50 40 30 20 10 0
0
200 400 600 800 1000 Concentration(ng mL–1)
(B) 6 × 105
Original runs Original + reruns replaced
4 × 105
2 × 105
0 0
1000
2000
3000
4000
Sample number
(C) SMC16:0 (ng mL–1)
Sphingomyelins
Arearatio (Analyte/IS)
were determined by fitting the identified sphingomyelin and dihydrosphingomyelin species to these standard curves based on acyl-chain length (i.e., concentrations of C16:0 and C16:1 were both determined based on the C16:0 standard curve). Instrument efficiency was closely monitored using a series of internal standards run daily. Area under the curve for these standards was plotted weekly to track instrument efficiency. Runs were stopped when consistent deviations of more than 30% from the median internal standard values were noted, and the instrument was recalibrated. Individual samples in which the internal standard deviated more than 30% of the median internal standard value were rerun. Using these criteria, approximately 20% of samples required reanalysis. Data that produced internal standard values closest to the overall median value were incorporated in the final analysis (Fig. 3B). Instrument control and quantification were performed using Analyst 1.4.2 and MultiQuant software (AB Sciex Inc., Thornhill, ON, Canada). Intra-day coefficient of variation (CV) was determined by analyzing 5 pooled samples 6 times. Intra-day CVs were as follows: C16:0 (2.1%), C18:0(5.7%), C20:0 (2.1%), C22:0 (3.0%), C24:0 (3.6%), C16:1 (2.1%), C18:1 (3.6%), C20:1 (9.6%), C22:1 (4.0%), C24:1 (2.4%) and dihydrosphingomyelins (DHSM) C16:0 (4.0%), C18:0 (3.7%), C22:0 (7.4%), and C24:0 (8.6%). Inter-day CVs were
Analyte Area (C12:0)
Berlin, MA, USA) and stored at 80 °C. Dried extracts were resuspended in pure methanol just prior to analysis. Analyses of sphingomyelins were performed on a triple quadrupole mass spectrometer (API3000, AB Sciex Inc., Thornhill, Ontario, Canada) using instrument parameters similar to those described in previous studies (Bandaru et al., 2007, 2011, 2013). Samples were injected using an Agilent 1100 high-pressure liquid chromatography (HPLC) (Agilent Technologies, Inc., Santa Clara CA, USA) equipped with a reverse phase C18 column (Phenomenex, Torrance, CA, USA). Sphingomyelin species were separated by gradient elution at the flow rate of 0.7 mL min 1. Mobile phases consisted of A: 60% methanol, 39% H2O, 1% formic acid with 5 mM ammonium formate; and B: 99% methanol, 1% formic acid, and 5 mM ammonium formate. Gradient conditions were as follows: 60% B for 0.01 min, a gradual increase to 100% B over the next 0.49 min, and hold at 100% B for 3 min. Decline from 100% to 0% B during the next 0.01 min, hold at 0% B for 0.99 min, increase from 0% to 60% B for 0.5 min and hold at 60% B for the final 0.5 min. The eluted sample was injected into the ion source where the detection of each sphingomyelin species was conducted by multiple reaction monitoring (MRM) in positive mode. Detailed MRM transitions of individual molecular species for sphingomyelin precursor and fragment ions are provided in Table 5. The ion spray voltage (V) was 5500 at a temperature of 80 °C with a nebulizer gas of 9 psi, curtain gas of 8 psi, and the collision gas set at 10 psi. The declustering potential was 60 V, the focusing potential 300 V, the entrance potential 10 V, the collision energy 30 V, and the collision cell exit potential 10 V. MS/MS scanned from 300 to 1000 atomic mass units (amu) per second with steps of 0.1 amu. Eight-point calibration curves (0.1 to 1000 ng mL 1) were constructed by plotting the area under the curve for sphingomyelin C16:0, C18:0, C20:0, C22:0, and C24:0 (Avanti polar lipids, Alabaster, AL, USA) prepared in pure methanol normalized to the C12:0 internal standard. The correlation coefficients (R2) obtained were >0.999 (Fig. 3A). Concentrations of sphingomyelins in each sample
2.0 × 106 1.5 × 106 1.0 × 106 5.0 × 105 0.0
1963
1986
2009
Year of sample collection Fig. 3 (A) Five-point standard curves for the indicated SM species. (B) Initial and rerun data showing values obtained for the internal standard SM C12:0. (C) SM C16:0 scatterplot by date of visit showing a random distribution, suggesting that SM was stable during long-term storage of plasma.
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
120 Plasma Sphingomyelins by age and sex, M. M. Mielke et al. determined using five repeat measurements of 10 pooled samples over a 3-month period (the time required for analysis of all study samples). Interday CVs for sphingomyelin species were as follows: C18:0/C16:0 (12%), C18:0/C18:0 (20%), C18:0/C20:0 (24%), C18:0/C22:0 (12%), C18:0/C24:0 (8%),C18:0/C16:1 (12%), C18:0/C18:1 (16%), C18:0/ C20:1 (20%), C18:0/C22:1 (28%), C18:0/C24:1 (12%),C18:1/C16:0 (14%), C18:1/C18:0 (8%), and C18:1/C24:0 (6%). Recovery was determined by comparing C12:0 internal standard levels in extracted plasma to equal amounts of C12:0 sphingomyelin prepared in pure methanol. The average extraction recovery obtained was 93%. As the dates of sample collection for this study ranged from 1968 to 2009 and were run in random order over the 3 months, we estimated sample stability by arranging data for each sphingomyelin species by date of study visit. We reasoned that sample degradation or continued enzymatic activity would be manifest by increasing or decreasing trends in analyte concentrations coincident with storage time. When arranged by date of visit, each species showed a random scatter (Fig. 3C shows sphingomyelin C16:0), suggesting that sphingomyelin content of plasma was stable with long-term 80 °C storage.
Research Program of the National Institutes of Health/National Institute on Aging.
Author contributions MMM conceived the analyses and contributed to the statistical analyses, interpretation of results, and wrote the initial draft of the paper. VVRB contributed to the mass spectrometry analyses and interpretation of the data. DH and YA contributed to the statistical analyses, interpretation of results, and writing the manuscript. RS and LF contributed to the study design of the Baltimore Longitudinal Study on Aging, data collection, interpretation of results and writing of the manuscript. NH contributed to the mass spectrometry analyses, interpretation of results and writing of the manuscript.
Conflict of interest The authors have no conflict of interests to declare.
References Statistical analyses Sex differences in baseline demographic and health-related characteristics were examined using Fisher’s exact test for categorical variables and t-tests or ANOVA for continuous variables. We examined all sphingomyelin species for outliers. As normal ranges for sphingomyelins in plasma are not yet known, we conservatively defined an outlier as any sphingomyelin species with a concentration of more than 3 interquartile ranges (IQR, 25th percentile–75th percentile) from the median. Within each class of the plasma sphingomyelin and dihydrosphingomyelin, a mean of 3% of samples were outside this range and excluded from the analyses. Some individuals had missing covariates for a specific visit. Missing values for the covariates described below ranged from 0% to 20.1% (for APOE genotype only) of the 3972 total visits. As the BLSA follows individuals for decades, we imputed the missing covariates for each individual and visit using information from neighboring visits. This allowed us to utilize the largest number of samples. We used linear mixed models to account for the longitudinal nature of the data and to model the trajectories for individual sphingomyelin species over time using time-dependent analyses. We used sphingomyelin class and species as the dependent variable. Based on the literature, we initially examined the following predictors: age, age squared, sex, race (white vs. African American), education, the presence of an APOE e4 allele (yes vs. no), BMI, WHR, smoking (never versus ever), depressive symptoms, triglycerides, cholesterol, medications (e.g., statins), and medical conditions (e.g., diabetes, hypertension, CKD), and the interaction term between these predictors and age. We forced age, age squared, sex, and BMI into all models based on the literature and our observations of significant associations with the sphingomyelins in univariate analyses. We then used backward selection with P < 0.10 to determine which variables to include in the final model for each sphingomyelin class and species. All analyses were conducted using STATA Version 11.0 (StataCorp, College Station, TX, USA).
Acknowledgments This work was supported by a grant from the National Institutes of Health/National Institute on Aging (U01 AG37526) and by the Intramural
Bandaru VV, McArthur JC, Sacktor N, Cutler RG, Knapp EL, Mattson MP, Haughey NJ (2007) Associative and predictive biomarkers of dementia in HIV-1-infected patients. Neurology 68, 1481–1487. Bandaru VV, Patel N, Ewaleifoh O, Haughey NJ (2011) A failure to normalize biochemical and metabolic insults during morphine withdrawal disrupts synaptic repair in mice transgenic for HIV-gp120. J. Neuroimmune Pharmacol. 6, 640– 649. Bandaru VV, Mielke MM, Sacktor N, McArthur JC, Grant I, Letendre S, Chang L, Wojna V, Pardo C, Calabresi P, Munsaka S, Haughey NJ (2013) A lipid storage-like disorder contributes to cognitive decline in HIV-infected subjects. Neurology 81, 1492–1499. Contreras FX, Ernst AM, Haberkant P, Bjorkholm P, Lindahl E, Gonen B, Tischer C, Elofsson A, von Heijne G, Thiele C, Pepperkok R, Wieland F, Brugger B (2012) Molecular recognition of a single sphingolipid species by a protein’s transmembrane domain. Nature 481, 525–529. Deeley JM, Hankin JA, Friedrich MG, Murphy RC, Truscott RJ, Mitchell TW, Blanksby SJ (2010) Sphingolipid distribution changes with age in the human lens. J. Lipid Res. 51, 2753–2760. Fernandez C, Sandin M, Sampaio JL, Almgren P, Narkiewicz K, Hoffmann M, Hedner T, Wahlstrand B, Simons K, Shevchenko A, James P, Melander O (2013) Plasma lipid composition and risk of developing cardiovascular disease. PLoS ONE 8, e71846. Gatt S, Bierman EL (1980) Sphingomyelin suppresses the binding and utilization of low density lipoproteins by skin fibroblasts. J. Biol. Chem. 255, 3371–3376. Gonzalez-Covarrubias V, Beekman M, Uh HW, Dane A, Troost J, Paliukhovich I, van der Kloet FM, Houwing-Duistermaat J, Vreeken RJ, Hankemeier T, Slagboom EP (2013) Lipidomics of familial longevity. Aging Cell 12, 426–434. Hammad SM, Pierce JS, Soodavar F, Smith KJ, Al Gadban MM, Rembiesa B, Klein RL, Hannun YA, Bielawski J, Bielawska A (2010) Blood sphingolipidomics in healthy humans: impact of sample collection methodology. J. Lipid Res. 51, 3074–3087. Hannun YA, Obeid LM (2011) Many ceramides. J. Biol. Chem. 286, 27855–27862. Haughey NJ, Cutler RG, Tamara A, McArthur JC, Vargas DL, Pardo CA, Turchan J, Nath A, Mattson MP (2004) Perturbation of sphingolipid metabolism and ceramide production in HIV-dementia. Ann. Neurol. 55, 257–267. Haughey NJ, Bandaru VV, Bae M, Mattson MP (2010) Roles for dysfunctional sphingolipid metabolism in Alzheimer’s disease neuropathogenesis. Biochim. Biophys. Acta 1801, 878–886. Hixson JE, Vernier DT (1990) Restriction isotyping of human apolipoprotein E by gene amplification and cleavage with HhaI. J. Lipid Res. 31, 545–548. Holzer M, Trieb M, Konya V, Wadsack C, Heinemann A, Marsche G (2013) Aging affects high-density lipoprotein composition and function. Biochim. Biophys. Acta 1831, 1442–1448. Jiang XC, Paultre F, Pearson TA, Reed RG, Francis CK, Lin M, Berglund L, Tall AR (2000) Plasma sphingomyelin level as a risk factor for coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 20, 2614–2618.
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Plasma Sphingomyelins by age and sex, M. M. Mielke et al. 121 Kim IC, Lee JH, Bang G, Choi SH, Kim YH, Kim KP, Kim HK, Ro J (2013) Lipid profiles for HER2-positive breast cancer. Anticancer Res. 33, 2467–2472. Koch W, Ehrenhaft A, Griesser K, Pfeufer A, Muller J, Schomig A, Kastrati A (2002) TaqMan systems for genotyping of disease-related polymorphisms present in the gene encoding apolipoprotein E. Clin. Chem. Lab. Med. 40, 1123–1131. Kuikka M, Ramstedt B, Ohvo-Rekila H, Tuuf J, Slotte JP (2001) Membrane properties of D-erythro-N-acyl sphingomyelins and their corresponding dihydro species. Biophys. J . 80, 2327–2337. Mielke MM, Haughey NJ, Bandaru VV, Weinberg DD, Darby E, Zaidi N, Pavlik V, Doody RS, Lyketsos CG (2011) Plasma sphingomyelins are associated with cognitive progression in Alzheimer’s disease. J. Alzheimers Dis. 27, 259–269. Milhas D, Clarke CJ, Hannun YA (2010) Sphingomyelin metabolism at the plasma membrane: implications for bioactive sphingolipids. FEBS Lett. 584, 1887–1894. Montefusco DJ, Chen L, Matmati N, Lu S, Newcomb B, Cooper GF, Hannun YA, Lu X (2013) Distinct signaling roles of ceramide species in yeast revealed through systematic perturbation and systems biology analyses. Sci. Signal 6, rs14. Nelson JC, Jiang XC, Tabas I, Tall A, Shea S (2006) Plasma sphingomyelin and subclinical atherosclerosis: findings from the multi-ethnic study of atherosclerosis. Am. J. Epidemiol. 163, 903–912. Petersen NH, Olsen OD, Groth-Pedersen L, Ellegaard AM, Bilgin M, Redmer S, Ostenfeld MS, Ulanet D, Dovmark TH, Lonborg A, Vindelov SD, Hanahan D, Arenz C, Ejsing CS, Kirkegaard T, Rohde M, Nylandsted J, Jaattela M (2013) Transformation-associated changes in sphingolipid metabolism sensitize cells to lysosomal cell death induced by inhibitors of acid sphingomyelinase. Cancer Cell 24, 379–393. Quinn PJ, Wolf C (2009) The liquid-ordered phase in membranes. Biochim. Biophys. Acta 1788, 33–46. Schlitt A, Blankenberg S, Yan D, von Gizycki H, Buerke M, Werdan K, Bickel C, Lackner KJ, Meyer J, Rupprecht HJ, Jiang XC (2006) Further evaluation of plasma sphingomyelin levels as a risk factor for coronary artery disease. Nutr. Metab. 3, 5. Shimokata H, Tobin JD, Muller DC, Elahi D, Coon PJ, Andres R (1989) Studies in the distribution of body fat: I. Effects of age, sex, and obesity. J. Gerontol. 44, M66–M73.
Shock NW, Greulich RC, Andres R, Arenberg D, Costa PT Jr, Lakatta EG, Tobin JD (1984) Normal Human Aging: The Baltimore Longitudinal Study of Aging. Washington, DC: U.S. Government Printing Office. Simons K, Ikonen E (1997) Functional rafts in cell membranes. Nature 387, 569– 572. Simons K, Vaz WL (2004) Model systems, lipid rafts, and cell membranes. Annu. Rev. Biophys. Biomol. Struct. 33, 269–295. Slotte JP, Bierman EL (1988) Depletion of plasma-membrane sphingomyelin rapidly alters the distribution of cholesterol between plasma membranes and intracellular cholesterol pools in cultured fibroblasts. Biochem. J. 250, 653–658. Vaarhorst AA, Beekman M, Suchiman EH, van Heemst D, Houwing-Duistermaat JJ, Westendorp RG, Slagboom PE, Heijmans BT (2011) Lipid metabolism in long-lived families: the Leiden Longevity Study. Age (Dordr) 33, 219–227. Vieira CR, Munoz-Olaya JM, Sot J, Jimenez-Baranda S, Izquierdo-Useros N, Abad JL, Apellaniz B, Delgado R, Martinez-Picado J, Alonso A, Casas J, Nieva JL, Fabrias G, Manes S, Goni FM (2010) Dihydrosphingomyelin impairs HIV-1 infection by rigidifying liquid-ordered membrane domains. Chem. Biol. 17, 766–775. Vouk K, Hevir N, Ribic-Pucelj M, Haarpaintner G, Scherb H, Osredkar J, Moller G, Prehn C, Rizner TL, Adamski J (2012) Discovery of phosphatidylcholines and sphingomyelins as biomarkers for ovarian endometriosis. Hum. Reprod. 27, 2955–2965. Weir JM, Wong G, Barlow CK, Greeve MA, Kowalczyk A, Almasy L, Comuzzie AG, Mahaney MC, Jowett JB, Shaw J, Curran JE, Blangero J, Meikle PJ (2013) Plasma lipid profiling in a large population-based cohort. J. Lipid Res. 54, 2898– 2908. Yeboah J, McNamara C, Jiang XC, Tabas I, Herrington DM, Burke GL, Shea S (2010) Association of plasma sphingomyelin levels and incident coronary heart disease events in an adult population: Multi-Ethnic Study of Atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 30, 628–633. Yu Z, Zhai G, Singmann P, He Y, Xu T, Prehn C, Romisch-Margl W, Lattka E, Gieger C, Soranzo N, Heinrich J, Standl M, Thiering E, Mittelstrass K, Wichmann HE, Peters A, Suhre K, Li Y, Adamski J, Spector TD, Illig T, Wang-Sattler R (2012) Human serum metabolic profiles are age dependent. Aging Cell 11, 960–967.
ª 2014 The Authors. Aging Cell published by the Anatomical Society and John Wiley & Sons Ltd.
Copyright of Aging Cell is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.
